{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "0b9ffbb2-8d33-eb0c-5a42-0733bdc3b74b",
    "_uuid": "e14f65723f5ac826104e861c45e58525740d8b2a"
   },
   "source": [
    "# Bikeshare数据集上的数据探索\n",
    "\n",
    "1、\t任务描述\n",
    "请在Capital Bikeshare （美国Washington, D.C.的一个共享单车公司）提供的自行车数据上进行回归分析。根据每天的天气信息，预测该天的单车共享骑行量。\n",
    "\n",
    "原始数据集地址：http://archive.ics.uci.edu/ml/datasets/Bike+Sharing+Dataset\n",
    "1)\t文件说明\n",
    "day.csv: 按天计的单车共享次数（作业只需使用该文件）\n",
    "hour.csv: 按小时计的单车共享次数（无需理会）\n",
    "readme：数据说明文件\n",
    "\n",
    "2)\t字段说明\n",
    "Instant记录号\n",
    "Dteday：日期\n",
    "Season：季节（1=春天、2=夏天、3=秋天、4=冬天）\n",
    "yr：年份，(0: 2011, 1:2012)\n",
    "mnth：月份( 1 to 12)\n",
    "hr：小时 (0 to 23)  （只在hour.csv有，作业忽略此字段）\n",
    "holiday：是否是节假日\n",
    "weekday：星期中的哪天，取值为0～6\n",
    "workingday：是否工作日\n",
    "1=工作日 （是否为工作日，1为工作日，0为非周末或节假日\n",
    "weathersit：天气（1：晴天，多云 ",
    "2：雾天，阴天 ",
    "3：小雪，小雨 ",
    "4：大雨，大雪，大雾）\n",
    "temp：气温摄氏度\n",
    "atemp：体感温度\n",
    "hum：湿度\n",
    "windspeed：风速\n",
    "casual：非注册用户个数\n",
    "registered：注册用户个数\n",
    "cnt：给定日期（天）时间（每小时）总租车人数，响应变量y （cnt = casual + registered）\n",
    "\n",
    "casual、registered和cnt三个特征均为要预测的y，作业里只需对cnt进行预测"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 导入必要的工具包"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "2ba3154a-c2aa-3158-1984-63ad2c0c786a",
    "_uuid": "5eb696b95780825e94ddb49787f9fa339fc3833b"
   },
   "outputs": [],
   "source": [
    "# 数据读取及基本处理\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "# plotting\n",
    "import seaborn as sn\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "# setting params\n",
    "params = {'legend.fontsize': 'x-large',\n",
    "          'figure.figsize': (30, 10),\n",
    "          'axes.labelsize': 'x-large',\n",
    "          'axes.titlesize':'x-large',\n",
    "          'xtick.labelsize':'x-large',\n",
    "          'ytick.labelsize':'x-large'}\n",
    "\n",
    "sn.set_style('whitegrid')\n",
    "sn.set_context('talk')\n",
    "\n",
    "plt.rcParams.update(params)\n",
    "pd.options.display.max_colwidth = 600\n",
    "\n",
    "# pandas display data frames as tables\n",
    "from IPython.display import display, HTML"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 读入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "_cell_guid": "21fa35be-878b-b4f2-ef6e-68dc070b8bfa",
    "_uuid": "73aee228226be55c0c8a6e4fcbf818c56cd94926"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 读入数据\n",
    "train = pd.read_csv(\"day.csv\")\n",
    "train.head()\n",
    "#print(\"train : \" + str(train.shape))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.4+ KB\n"
     ]
    }
   ],
   "source": [
    "train.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "没有缺失数据"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "_cell_guid": "be11c1c7-acfe-cb9a-bc81-95d461b884c5",
    "_uuid": "b53750ea6a3a2a02aa4297f5401e29c2f4d92e31"
   },
   "source": [
    "## 数据探索"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>mnth</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>cnt</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>848.176471</td>\n",
       "      <td>3656.172367</td>\n",
       "      <td>4504.348837</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>211.165812</td>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>686.622488</td>\n",
       "      <td>1560.256377</td>\n",
       "      <td>1937.211452</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>22.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>183.500000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>315.500000</td>\n",
       "      <td>2497.000000</td>\n",
       "      <td>3152.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>366.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>713.000000</td>\n",
       "      <td>3662.000000</td>\n",
       "      <td>4548.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>548.500000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>1096.000000</td>\n",
       "      <td>4776.500000</td>\n",
       "      <td>5956.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>3410.000000</td>\n",
       "      <td>6946.000000</td>\n",
       "      <td>8714.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          instant      season          yr        mnth     holiday     weekday  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean   366.000000    2.496580    0.500684    6.519836    0.028728    2.997264   \n",
       "std    211.165812    1.110807    0.500342    3.451913    0.167155    2.004787   \n",
       "min      1.000000    1.000000    0.000000    1.000000    0.000000    0.000000   \n",
       "25%    183.500000    2.000000    0.000000    4.000000    0.000000    1.000000   \n",
       "50%    366.000000    3.000000    1.000000    7.000000    0.000000    3.000000   \n",
       "75%    548.500000    3.000000    1.000000   10.000000    0.000000    5.000000   \n",
       "max    731.000000    4.000000    1.000000   12.000000    1.000000    6.000000   \n",
       "\n",
       "       workingday  weathersit        temp       atemp         hum   windspeed  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     0.683995    1.395349    0.495385    0.474354    0.627894    0.190486   \n",
       "std      0.465233    0.544894    0.183051    0.162961    0.142429    0.077498   \n",
       "min      0.000000    1.000000    0.059130    0.079070    0.000000    0.022392   \n",
       "25%      0.000000    1.000000    0.337083    0.337842    0.520000    0.134950   \n",
       "50%      1.000000    1.000000    0.498333    0.486733    0.626667    0.180975   \n",
       "75%      1.000000    2.000000    0.655417    0.608602    0.730209    0.233214   \n",
       "max      1.000000    3.000000    0.861667    0.840896    0.972500    0.507463   \n",
       "\n",
       "            casual   registered          cnt  \n",
       "count   731.000000   731.000000   731.000000  \n",
       "mean    848.176471  3656.172367  4504.348837  \n",
       "std     686.622488  1560.256377  1937.211452  \n",
       "min       2.000000    20.000000    22.000000  \n",
       "25%     315.500000  2497.000000  3152.000000  \n",
       "50%     713.000000  3662.000000  4548.000000  \n",
       "75%    1096.000000  4776.500000  5956.000000  \n",
       "max    3410.000000  6946.000000  8714.000000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#对数据值型特征，用常用统计量观察其分布\n",
    "train.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1. 离散特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "season属性的不同取值和出现的次数\n",
      "3    188\n",
      "2    184\n",
      "1    181\n",
      "4    178\n",
      "Name: season, dtype: int64\n",
      "\n",
      "mnth属性的不同取值和出现的次数\n",
      "12    62\n",
      "10    62\n",
      "8     62\n",
      "7     62\n",
      "5     62\n",
      "3     62\n",
      "1     62\n",
      "11    60\n",
      "9     60\n",
      "6     60\n",
      "4     60\n",
      "2     57\n",
      "Name: mnth, dtype: int64\n",
      "\n",
      "weathersit属性的不同取值和出现的次数\n",
      "1    463\n",
      "2    247\n",
      "3     21\n",
      "Name: weathersit, dtype: int64\n",
      "\n",
      "weekday属性的不同取值和出现的次数\n",
      "6    105\n",
      "1    105\n",
      "0    105\n",
      "5    104\n",
      "4    104\n",
      "3    104\n",
      "2    104\n",
      "Name: weekday, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "#对类别型特征，观察其取值范围及直方图\n",
    "categorical_features = ['season','mnth','weathersit','weekday']\n",
    "for col in categorical_features:\n",
    "    print( '\\n%s属性的不同取值和出现的次数'%col)\n",
    "    print(train[col].value_counts()) \n",
    "    train[col] = train[col].astype('object')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "类别型特征的取值不多\n",
    "类别型特征可以采用独热编码（One hot encoding）/哑编码"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 2. 数值特征的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x000001F777725E48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F77743E320>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x000001F777622A20>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x000001F7777C7160>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#对数值型特征，直方图\n",
    "numerical_features = ['temp','atemp','hum','windspeed']\n",
    "train[numerical_features].hist()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 3. 特征与目标之间的关系"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.1 每年骑行量的分布\n",
    "violinplot中用x表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f777824390>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['yr',\n",
    "                        'cnt']],\n",
    "              x=\"yr\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2011年和2012年的分布差异很大"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.2 一年中每天的骑车量\n",
    "用颜色参数hue表示类别（年）信息"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'dayly distribution of counts')]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import datetime\n",
    "\n",
    "train['date'] = pd.to_datetime(train['dteday'])\n",
    "train['dayofyear'] = train[\"date\"].dt.dayofyear  #减今年的第几天\n",
    "\n",
    "fig,ax = plt.subplots()\n",
    "sn.pointplot(data=train[['dayofyear',\n",
    "                           'cnt',\n",
    "                           'yr']],\n",
    "             x='dayofyear',y='cnt',\n",
    "             hue='yr',ax=ax)\n",
    "ax.set(title=\"dayly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "每年开始和结束的数量少，中间多，骑行量和季节/月份有关"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.3 季节与骑车数量的关系\n",
    "violinplot得到详细分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7794b80f0>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sn.violinplot(data=train[['season',\n",
    "                        'cnt']],\n",
    "              x=\"season\",y=\"cnt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "能看出来每个季节骑行量的分布不同\n",
    "barplot利用矩阵条的高度反映数值变量的集中趋势，以及使用errorbar功能（差棒图）来估计变量之间的差值统计。\n",
    "谨记barplot展示的是某种变量分布的平均值"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Seasonly distribution of counts')]"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['season',\n",
    "                       'cnt']],\n",
    "           x=\"season\",y=\"cnt\")\n",
    "ax.set(title=\"Seasonly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "骑行量和季节的关系就很明显了：第2、3、4季度的骑行量明显高于第1季度"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.4 月份与骑车数量的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'Monthly distribution of counts')]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['mnth',\n",
    "                       'cnt']],\n",
    "           x=\"mnth\",y=\"cnt\")\n",
    "ax.set(title=\"Monthly distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.5 天气和骑车数目的关系"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[Text(0.5,1,'weathersit distribution of counts')]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,ax = plt.subplots()\n",
    "sn.barplot(data=train[['weathersit',\n",
    "                       'cnt']],\n",
    "           x=\"weathersit\",y=\"cnt\")\n",
    "ax.set(title=\"weathersit distribution of counts\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.6 工作日和节假日的分布"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "F:\\deeplearn\\lib\\site-packages\\scipy\\stats\\stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
      "  return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f77977a3c8>"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5zncYOXIko0ePZuLEibz88svlZtY93EXjKIC7LwIuBM4ys33T4ve4++PuvpTguusRqMMlBSinEx3STAD6Ekx6+CXBpIa6LOvVAeUECYcs6+xpvNUSiQTunjU2bNiwtuyy06upqWHFihWt3m7YsGEFPc1zUGWM7x2/81D624YGXvrTYk4/40xeeOEFampq6N27d2N7Ovjgg6mrq4NgFODvZBkFMLPUKMBKgrOjWWnx9WaWGgXQ0LQUlLz8rXD3P7n7b4GLgM8THJTZkkc5wdnS9rTltsRFcuLiZ3Yw+fk4vXtVcsEFF7Bhwwa6detGjx49GtepqKigrKwMNAog0kzOzpTMbCgw2t1/mVb8l/B3HdDDzHq6+5Zw/V7sPBhTF3QHAx+lbT84jK8nSD6DM152ME2vU+2x4uJizKyFNTa3ZbedWkVFxW4+k12rbee6RNV3jilmSx3c804dU6dOZdy4cXTr1q3J51ZdXU1JSUmyvr4+UqMAGgFovUIfBcimpc8rkUjs9f5z+Xka8IswOaUcBSSBR4FtwPFpsRPCsiXuvgZ4Lz1uZsOB3sDr7p4EFmTE+wGHA691yLsRyeKgvkUcObiIqqu/ye9//3tWrlxJfX19s/Xi8XgMjQKINJPLa0qvAkuAh8zsCqA/8FPgp+7+vpndC8wOp3rHgDuAO9091cmeBVSZ2fvAGuBe4IlwOjjA7cBjZrYMqCaYQPFnd9eYexdTHIP+3RKsqy0GYEC3OMUdOE9tXU2Stz5Kcvy+O/t4B+4f9L3Kysqoqalh69atVFZWAkFPM0xUERwF0AhAa3WVUYCUlj6v6urqvT5byuWU8B3A6QTTuP8P+DnwLHBluMo04EXgaYLJD08SfC8jZRZwN8FdIH4H/BX4Rtr+nwSuAW4EXgfqCaacSxcTi8GEg7bQrzxBv/IE5x+0lVgHJqUVm5N8f36cdTXJxrI33n6PWCzGaaedRkVFBYsXL26Mvfnmm5SXl4NGAUSayensu/DLfmftIlYHTAp/ssWTBHd/uC5bPFxnNjB772sqnd3wvvXcPGZ9Tl7rMwNiHNAnxg9fizP5s8V8XAt33HofX/3qVxkyZAjnnHMOVVVV/OAHPyCZTDJnzhxOOeWUultvvVWjACIZ8jUlXKRglBTF+P6JJdy1KM63X4xTUgRfPnUE0773PQCmTJlCbW0tkyZNori4mLFjxzJ+/Pj0UZ9ZwD4EowClwDMEdzgBglEAM0uNAvQGXkajAFKglJRE2kH/ihjX/3Np4/KACyakpn1TVlZGVVUVVVVVQPNxd40CiOzU1WYziohIhCkpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZOxxUjKz98J7bmWWf9LM1rZvtURyY8KT9WyuSzYr/6gmyTm/aH53bxHpWC3e0cHMxrHzkcv7AzeYWebt8g/sgHqJdJjXVjbwxtoGAD7cCg8uTdCtuOk6q7Y2T1Qi0vF2d5uhJcBVBI+SABhJcPftlCSwFbiw/asm0jH+qXeMX72VJJV23tmQpDQtKcWAbiVwzVjdhUsk11o86tz9feBkADObA1zp7l3vgStSUAZWxvjR54L71P3o9eDO3j1KO/DZFiKyx/a4K+juE82sxMwOJLiTcSwj/tf2rpxIR7tmbAmJhiSrtiSJNzSP7/cJJSuRXNrjpGRmpwFzgH5kJCSCYbziZhuJRNyCVQ3c/HqczfWQzLiMFIvBvHPL8lMxkS6qNYPmMwkev3wDXfGZyVKQ7q9OMHxAjAnDi6nQEJ5I3rUmKR0IfM3d3+qoyojk2uotSaYfV8q+GqYTiYTWfHn2j8DwjqqISD7YPjH+vknTv0WiojVnSr8CfmpmJwFv03RqOO5+V3tWTCQXjh1axG1/jLPkwyKG9opRktFNG3ewLpWK5FJrktK3gU3AqeEPBBMcYuFvJSXpdH75VoLKUli4uoGFq8PCVIuOKSmJ5FprktJBwPXAKnf/CYCZLQCeBf6rA+om0uEeGFfK3GUJ+nWPcXqYgC5/YQejhxRx7qd1a0iRXGvNUfcj4Dzg3bSyOwnu5lDVnpUSyZWf/jnB7/7RwCd77pzoMO7gIn7zXoIHlybyWDORrqk1SWk8cJ67/yZV4O4PESSli9q5XiI58fI/GvjuMSWMGrzzUPj8AcVMHVvCi+9l+TatiHSo1iSl7gT3ucu0EfhE+1RHJLfqEtA9yyB2ZRls25H7+oh0da1JSi8DPzSzvqkCM+tNcD3pf9u5XiI5MWJgjHv/nGjy+Iqt9UnmLElwxAB9d0kk11oz0eEK4LfAKjN7n2B+0n7AO8C/dkDdRDrcpZ8tYervdjD+VzsY2CMoW7sNPtkzxg0n6C7hIrnWmhuyLjezw4HPA4cSfE/pbeBFd9fgu3RKA3rEuPe0Uv60Jsnyj5OUFMHQXjFGDY5RFNOZkkiutaor6O71BFPAn+2Y6ojkXmlxjKOHxDh6SL5rIiL6IoaIiESGkpKIiESGkpKIiESGkpKIiERGTue8mtlQ4FbgJCBOMGHianffZGa9CG5bdDpQS3CD1++7ezLctgT4MXBuWO+5wJRw8kVq/9OBS4GewJPAZe6+KUdvT0RE9lLOkpKZFRMkirUESak7cDfwIMH3nO4DhgEnhr8fBDYAs8NdzCS4O/k4oCyM1wNTwv1PJvgu1QXAeuCn4T7P7uj3Jl3bupokdy+Os+TDJMUxOHpIEdPP3EY3YOvWrdxwww28/PLLlJeXM378eI499tjGbdXZEmkql8N3I4EjgYnuvszd/0iQRMaZ2X4EyeMSd69296cJ7kj+bQAz6wZMJjiret3dXwEuByaZWfdw/1cDM9x9nrsvIrgn31lmtm8O36N0MYmGJNe/Eqd2B9x8SglVJ5Tw7sYk1/3oHgCmT5/O8uXLmTt3Lt///vd56KGHmDdvXvou0jtbZ4S/Z6aCaZ2tiQSducMIOlsiBSmXSenvwKnuviatLHVvl7HAx+6+LC32CnCAmQ0GRgA9gFcz4j2AEWY2CDggPR7uayNwLCId5J2NSf62IcmUsSV8qk8Rh+xTxORRxfzvH/7EqlWrmDdvHlVVVRx66KGcfPLJXH755Tz33HOAOlsi2eRs+M7d1wMvZBR/m+A2RUOAVRmxD8LfQ8P4Nnf/OG1/m82sJoynhjqy7WNoW+qbSCRw96yxYcOGtWWXnV5NTQ0rVqxo9XbDhg0r2Bk1gytjzDyphL7dd979IXUjiAULFlBRUQHQ2Jb69+/Phx9+yIYNG2LsvrP1d7J0tsws1dla3mFvTCRP8nZzLzP7DnAWwcSGI4G6jFVSy+VARZZ4ap1UnCzrpOIiHaJXeYyjPtn0dkT/81aCfT85kLVr19KvX78msb59g/sZr1+/vog8dLZg1x0udbZar5A7XLvS0ueVSOz9M8jykpTM7DqCBwNe6e7PmtmhNE8eqeUaYHuWeGqdVDy1vCVLvNWKi4sxsxbW2NyW3XZqFRUVu/lMdq22nesSVY+9keD/lieZVXU+f/toBz179mzymW3dGjz9JR6PgzpbIs3kPCmZ2W0EF24nu/vdYfFKYHDGqqnl1QSz7XqYWU933xLupxfBQbsq3D61zUcZ+8jsZYp0iIeXBU+rnTyqmH8+eiQrfreU+vr6JuuklsvKyiAPnS3YXYdLna3W6iodrpSWPq/q6uq9PlvK6ZmnmVURXMi9MC0hAbwG9DWzw9LKTgDeCydGLAG2AcdnxLcBS8J13kuPm9lwoDfweke8F5F0dy2K89DSBFccVcxXDikGYNCgQaxbt67JemvXrgWgb9++DQSdqR5m1jMVb6GzlU6dLSlYufye0khgOnAz8JtwxlzKauDXwENm9k2Cg+6GcH3cfbuZ3QvMNrMLgRhwB3Cnu6c6KrOAqvBZT2uAe4En3F0Xg6VDPbAkzpNvN3DN2GI+f0BxY/nIkSPZtGkT77zzDgceeCAACxcuZODAgfTp0ydJ087Wc+Fm6Z2tWjNLdbaWgTpbUvhyOXx3FsGZ2dTwJ91w4OvATwhmGm0Gbsk4m5pG8IXbpwnuBvEzwqQVmgXsAzwAlALPEEy3Fekw72xo4NE3Gjj70CJGDS5iw/bgWw5FGzYx6DMj+dznPsfUqVOpqqpi3bp13HHHHZx9dvB9bnW2RJrL5ZTw7wHf281qX2th+zpgUviTLZ4Ergt/RHJi/vIGGpLw87828PO/pj3r8peX8fTTTzNz5kyuv/56zj//fCorK7nooos45phj0sfd1dkSSaPnPYvshYkjSpg4onn5gAvup9v+BwNw2223NYlVV1c3/ludLZGmutoUexERiTAlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiQwlJRERiYySfLyomcWA54Bn3X12Wvl04FKgJ/AkcJm7b0qLXwxMBwYCLwGT3H1lWnwccBPwKWBRGH+j49+RiIi0h5yfKZlZEXAH8MWM8snAFcBE4CTgMOC+tPiXgNuAacAYoBT4VZjgMLMjgMeB2cAo4O/AC2bWvYPfkggAyWSSa1/awa890aT87rvv5rjjjmPkyJFMmTKFbdu2NYmb2cVm9nczqzGzZ8xsaEZ8nJn91cy2m9l8M/t0x78bkfzIaVIyswOAV4AvA5sywlcDM9x9nrsvAi4EzjKzfdPi97j74+6+FDgXOAI4LoxfATzn7ne5+1+B/0eQuM7u0DclAjQkk8xelGDhB8km5T/72c+YO3cuN954Iw899BDvvvsu99xzT2NcnS2RpnJ9pjQGWAYcCXycKjSzQcABwKupMndfBmwEjg3PrsZkxNcDf2FnUjo2I74DeC0tLtIhPtiS5OrfxFmwqoHKsqaxOXPm8K1vfYvjjz+e4cOHc9NNN7FgwQLWrVsXC1dRZ0skTU6vKbn7I8AjAGaWHhoS/l6VsckHwFCgD1DRQjy1j2zx/dtS10QigbtnjQ0bNqwtu+z0ampqWLFiRau3GzZsWEHPqHnzowY+1TvGDSeUMPn5HY3ly5cvZ8WKFfTv379JW+rRowdvvfVWyZVXXpnqbM1Kxdx9vZmlOlvzCTpbd6fFd5hZqrM1t6Pfm0iu5WWiQxYV4e+6jPI6oHwP4ql9tBQX6RAnf6qYkz/VvHzt2rUA9OvXr0l579692bBhQxF56GzBrjtc6my1XqF3uLJp6fNKJBJZy1sjKklpe/i7HNiSVl4O1GTEyRJP7aOleKsUFxdnns1l2NyW3XZqFRUVu/lMdq22nevSmRx66KFUVlY2LpeWlrJjxw5QZ0ukmagkpdS07sHAR2nlgwl6iesJksvgjO0Gs/M60spdxDN7mSI5UV4e5I36+vom5fF4PBXLeWcLdtfhUmertbpah6ulz6u6unqvz5Yicebp7muA94DjU2VmNhzoDbzu7klgQUa8H3A4wWQGwt/p8VLgmLS4SE4NGjQIgHXr1jUp37hxI3379m2g5c5WqjOlzpZ0KZFISqFZQJWZnWZmo4AHgSfcfXkYvx243MwmhAnrEeDP7j4/jM8GzjCzq8zsMOC/CYY5nsjt2xAJ9O/fn2HDhrFo0aLGMnenpqaGgw8+OK7OlkhzURm+gyAp7QM8QDDl9Rlgciro7k+a2TXAjQRnUC8DX0mL/8nMzgvjM4GFwBfdvaudXUuETJgwgVmzZjFkyBD22WcfrrvuOsaMGUP//v1TX2i6HXjMzJYB1cDNNO9s/cHMrgJeJPg+kzpbUrDylpTcff+M5SRwXfizq21mExyku4o/gQ5WiZALLriAjRs3Mm3aNOLxOCeeeCJnnnlmY1ydLZGmonSmJNLpPXxG02/PxmIxrrrqKq666qrGssyLwepsiewUpWtKIiLSxSkpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCjJWH6zAAAItElEQVQpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZCgpiYhIZJTkuwLtycxKgB8D5xK8t7nAFHevz2vFRPaS2rZ0FQWVlICZwKnAOKAMeBCoB6bks1Ii7UBtW7qEghm+M7NuwGTgand/3d1fAS4HJplZ9/zWTqTt1LalKymYpASMAHoAr6aVvRKWjchLjUTah9q2dBmxZDKZ7zq0CzM7C3jQ3SszyrcBF7n7E3u6r8WLFzcAsV3Fi4qCXJ5oKIzPbk8UFwUfR0NDQ6u3LSoqIgYkk4l2rlV0xWLFJGnx80qOGjVqjzqFuWrbatet19Xa9h60a2hF286mkK4pVQB1WcrrgPJW7quB4Cxyc9Zg+B+yy6xVgBr24g/VzgbchT6xZIsHbS+CNranctK21a7bsn0Xa9stt2tofdtuppCS0nayH6DlQE1rdjRq1KhC+lyk81Pbli6jkK4prQR6mFnPVIGZ9SLoZa7KW61E9p7atnQZhZSUlgDbgOPTyk4Iy5bkpUYi7UNtW7qMgpnoAGBmtwL/ClxIMMj7EPC4u38nrxUT2Utq29JVFNr48jSgO/A0EAd+BkzPa41E2ofatnQJBXWmJCIinVshXVMSEZFOTklJREQiQ0lJREQiQ0lJREQiQ0lJREQiQ0lJREQio9C+p1Sw9OTRtjGzGPAc8Ky7z853faQpteu2K9S2rTOlziP9yaNnhL9n5rVGEWdmRcAdwBfzXRfZJbXrNijktq2k1AnoyaOtZ2YHEDwI78vApjxXR7JQu26bQm/bSkqdg5482npjgGXAkcDHea6LZKd23TYF3bZ1TalzGAJsc/fGBujum82sBhiav2pFl7s/AjwCYGZ5ro3sgtp1GxR629aZUufQnk8eFYkKtWtpRkmpc2i3J4+KRIjatTSjpNQ56MmjUojUrqUZJaXOQU8elUKkdi3NaKJDJ+Du283sXmC2maWePHoHcKe71+a3diJto3Yt2SgpdR568qgUIrVraUJPnhURkcjQNSUREYkMJSUREYkMJSUREYkMJSUREYkMJSUREYkMJSUREYkMJaUuxsySZvbl9tjezB4ws1+0sO4vzOyBtr6WSHszs3+Y2WW7iLXYntvhtXU87AF9eVb2xpUE38IXKQRqzxGgpCRtlv4cHJHOTu05GpSUuqbPmtl/AJ8B3gEuDR9FjZl9ApgBnAn0IXgq6JXu7pk7CYciKt397HB5AnA9MJjgIWRlGet/G/gWsD+wFXg2XN4BfABc4+7/nbb+S8B8d7++vd64RJ+ZVQOPuftN4fKdwEXAJ9w9bmaDCNrLgcBpwGXAvsBbwLXu/ly43f8CfwVOAvoR3Ow1/XUOAH4P/I+7X5bens3sonC/Pwe+DZQCLwDfdPdt4fZfBb4fvvbvgPeAXu5+URjX8dAGuqbUNX0L+E9gOMGB9IiZpYYtfkFwEI8HjgZqgRfNrKKlHZrZScD9wC0Ej2neCpyeFh8fvua/AwcR/JH5V4KDvC583fFp63+S4I/II3vzRqVTeh44OW35JIL74x0ZLv8LQQL6N+AGINXB+jXwlJkdkbbtxcBVwJfd/c1UYZjYfkOQCC7fRT0+Q3AH888B3yDoqE0Ktz+G4D59dxE8un0pQRJL7V/HQxvpTKlr+kFab/KHwHygv5kNIDgAj3L3RWH8POB94Dzg3hb2OQn4tbvfFW7378AX0+KrgYvc/Zlw+X0zewU4LFx+GHjJzAa6+4cEf3D+lO0MTQre88AVZlZGcLa+P8GZyPHAH4EvAM8RJJv/cvfHwu3+08yOBqYStFeAl9x9Xsb+ewMvAn8ALnH3Xd0AtBT4hruvAd4wsxeAUWHsMuBpd789XL7WzNITqY6HNtKZUtf0btq/N4W/uwOfBuqBxalgOFTx5zDWksOBP6VtlwQWpi2/AqwwsxnhLKQ3CYZeisNV5gMrgHPC5XMJeqLS9bxGMIQ1huAsaQHwEnB8eEb/eYLktA/wesa2/0fTtvouzX2PoL2ucPeGFuqxJUxIKZsJEhUEZ1ELM9ZPr4uOhzZSUuqaElnKYgSPp84mxp61lcyZS/Wpf4Rj9POBvgQ94X8DnkrFw4P2EeBrZnYQwZDIY0iX4+5x4LcEQ3gnAq+EP8cRDIV1I7i+k01mW83Wpl8FJgBXm9nwFqpSn6Us1cZ3sPtjQsdDGygpSbo3CS7GpoYoCK8lHUEwht+SpQTXoNIdmfbvS4Efufvk8OLtUoKx9PQDdy4wlmB8/aWMXqp0Lc8DpwDHEiSkhQRn81cDvwlnyq0maC/pjmH3bfUpd/8ZwdnXPWnXU1vjL6QdJ6Gj0v6t46GNdE1JGrn738zsl8AcM5sMbCSYPZRg972024H54dj5M8AFBMlsaRhfD5xkZocRHHjfJhg/X5b2+m+FM6/+nfCCsnRZzwN3E7S9P7j7DjN7jeCM4hvhOjcBVWa2kmDI+WsE15tO3MPXuJyg/X0T+Ekr63c78LqZXUFw1vY1ggT6t7S4joc20JmSZPo6wXj9UwQXgiuAf3b3DS1t5O5/IBj//gawhOAAezRtlSuBJLCIYGimHLiRpr1H2Dm76Jd79S6kU3P31QRn7ovcPTUE9wrBH/Dnw+XZwA/Dn2UEs9e+7O7z9/A13gZuBm4MZ+O1pn6LgIkEyWQp8FngScIhOh0Pbacnz0qkmNmPgKHuPn63K4vkSTjLb6u7v5FW9iywwN2r2vF1utzxoOE7iQQzG03wvalvEsxCEomy0cDUtK9MfIFgYsbU9th5Vz4elJQkKk4GrgPu2NPhF5E8uovg+1OPEXyX6k3g7PQzp73UZY8HDd+JiEhkaKKDiIhEhpKSiIhEhpKSiIhEhpKSiIhEhpKSiIhEhpKSiIhExv8HAPgZH8ZEsn4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig,(ax1,ax2) = plt.subplots(ncols=2)\n",
    "sn.barplot(data=train,x='holiday',y='cnt',ax=ax1)\n",
    "sn.barplot(data=train,x='workingday',y='cnt',ax=ax2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### 3.7 数值型特征和y之间的相关性"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1f7798c7eb8>"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "corrMatt = train[[\"temp\",\"atemp\",\n",
    "                  \"hum\",\"windspeed\",\n",
    "                  \"casual\",\"registered\",\n",
    "                  \"cnt\"]].corr()\n",
    "mask = np.array(corrMatt)\n",
    "mask[np.tril_indices_from(mask)] = False\n",
    "sn.heatmap(corrMatt, mask=mask,\n",
    "           vmax=.8, square=True,annot=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "体感温度和温度高度相关\n",
    "目标cnt与温度正相关、湿度和风速负相关"
   ]
  }
 ],
 "metadata": {
  "_change_revision": 0,
  "_is_fork": false,
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
